Maximizing adaptive power in neuroevolution

PloS One
Paolo PagliucaStefano Nolfi

Abstract

In this paper we compare systematically the most promising neuroevolutionary methods and two new original methods on the double-pole balancing problem with respect to: the ability to discover solutions that are robust to variations of the environment, the speed with which such solutions are found, and the ability to scale-up to more complex versions of the problem. The results indicate that the two original methods introduced in this paper and the Exponential Natural Evolutionary Strategy method largely outperform the other methods with respect to all considered criteria. The results collected in different experimental conditions also reveal the importance of regulating the selective pressure and the importance of exposing evolving agents to variable environmental conditions. The data collected and the results of the comparisons are used to identify the most effective methods and the most promising research directions.

References

Jan 1, 1997·Evolutionary Computation·D E Moriarty, R Miikkulainen
Aug 16, 2002·Evolutionary Computation·Kenneth O Stanley, Risto Miikkulainen
Jan 23, 2003·Artificial Life·Gregory S Hornby, Jordan B Pollack
Apr 7, 2005·Artificial Life·Andy PhilippidesMichael O'Shea
Feb 10, 2009·Artificial Life·Kenneth O StanleyJason Gauci
Jan 12, 2011·Proceedings of the National Academy of Sciences of the United States of America·Josh Bongard

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Citations

Mar 2, 2019·PloS One·Paolo Pagliuca, Stefano Nolfi
Jan 28, 2021·Frontiers in Robotics and AI·Paolo PagliucaStefano Nolfi
Apr 28, 2021·Scientific Reports·Nicola Milano, Stefano Nolfi

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Software Mentioned

HyperNEAT
PSHC
NeuroEvolution of Augmenting NEAT
PHSC
NeuroEvolution
xNES
NeuroEvolution of
NEAT
SANE
FARSA

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